DescriptionThis paper presents a neural network implementation of photometric stereo formulated as a regression task. Photometric stereo estimates the surface normals by measuring the irradiance of any visible given point under different lighting angles. Instead of the traditional setup, where the object has a fixed position and the illumination angles changes around the object, we use two constant light sources. In order to produce different illumination geometries, the object is moved under a multi-line scan camera. In this paper we show an approach where we present a multi-layer perceptron with a number of intensity vectors (i.e. points with constant albedo under different illumination angles) from randomly chosen pixels of six materials with different reflectance properties. We train it to estimate the gradient of the surface normal along the transport direction of the given point. This completely eliminates the need of knowing the light source configuration while still remaining a competitive accuracy even when presented with materials which have non-Lambertian surface properties. Due to the random pooling of the pixels our implementation is also independent from spatial information.
|12 Apr 2017
|OAGM/AAPR ARW 2017: Joint Workshop on “Vision, Automation & Robotics”
- neural networks
- light field
- surface normals